Quantitative medical imaging is a fairly recent technology field in medical imaging. Quantitative imaging is the extraction of quantifiable features from medical images for the assessment of normal or the severity, degree of change, or status of a disease, injury, or chronic condition relative to normal. Quantitative imaging includes the development, standardization, and optimization of anatomical, functional, and molecular imaging acquisition protocols, data analyses, display methods, and reporting structures. These features permit the validation of accurately and precisely obtained image-derived metrics with anatomically and physiologically relevant parameters, including treatment response and outcome, and the use of such metrics in research and patient care.
The quantitative calculated values are typically visualized as colours, either as pseudo colours on top of other medical image, or as colour images on their own. Examples of such images are shown in FIGS. 1 to 4. FIG. 1 shows an image of PET SUV (Positron Emission Tomography Standardized Uptake Value) uptake values. Positron emission tomography (PET) is a nuclear medicine, functional imaging technique that produces a three-dimensional image of functional processes in the body. The system detects pairs of gamma rays emitted indirectly by a positron-emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule. Three-dimensional images of tracer concentration within the body are then constructed by computer analysis. In modern PET-CT scanners, three dimensional imaging is often accomplished with the aid of a CT X-ray scan performed on the patient during the same session, in the same machine. If the biologically active molecule chosen for PET is fludeoxyglucose (FDG), an analogue of glucose, the concentrations of tracer imaged will indicate tissue metabolic activity by virtue of the regional glucose uptake. Use of this tracer to explore the possibility of cancer metastasis (i.e., spreading to other sites) is the most common type of PET scan in standard medical care (90% of current scans). However, on a minority basis, many other radioactive tracers are used in PET to image the tissue concentration of many other types of molecules of interest.
The colours are used to indicate specific calculated/measured numbers, and a scale is shown next to the image such that the radiologist can estimate the value from comparing the scale with the colours in the image. The dashed lines in the scale emphasize the different colours of the scale. There are, however, only two ranges where it is really possible to deduce the quantitative number from the colour image: around value 0.75 and around 1.75. For the other values there is hardly any visible difference in colour visible and therefore it is difficult to accurately deduce the quantitative value in a visual way.
FIG. 2 shows a calculation of Quantitative Dynamic Image Analysis for Contrast Enhanced MRI (Magnetic Resonance Imaging) and CT (Computed Tomography). Note the scale which is used to make it possible for the radiologist to deduce the quantitative value from the colour image. The dashed lines in the scale emphasize the different colours of the scale. Here, the blue and green parts of the scale are each larger than the red part of the scale. For the other values there is hardly any visible difference in colour visible and therefore it is difficult to accurately deduce the quantitative value in a visual way. Further, this scale is not perceptually uniform but consists mostly out of green and blue. It would be better for human perception to equally spread out all colours over the scale, which would improve ability to visually estimate the quantitative value being visualized.
Two more examples are shown in FIGS. 3 and 4. Both images are ultrasound images with additional quantitative information being visualized on top of the ultrasound. In FIG. 4 it concerns an elastography ultrasound image. Please note the different colour scale/colour coding used herein. Also here the scale is perceptually not uniform. The dashed lines in the scale emphasize the different colours of the scale. The scale contains more blue and red than e.g. yellow or green.
Current state-of-the-art uses well known colour coding/colour mappings/colour lookup tables (LUTs) to translate quantitative numbers into colours. Examples of such LUTs are fire LUTs, rainbow LUTs, hot iron LUTs, Hot/heated/black-body radiation colour scale, . . . . These images then are typically stored in a particular colour space (CIE 1931 XYZ; CIELUV; CIELAB; CIEUVW; sRGB; Adobe RGB; ICC PCS; Adobe Wide Gamut RGB; YIQ, YUV, YDbDr; YPbPr, YCbCr; xvYCC; CMYK; raw RGB colour triplets; . . . ) and visualized on a display system. Sometimes (in an ideal situation) there is a colour management module (such as the ICC colour management module (CMM)) present that will take care of the appropriate transformation of the image in a particular colour space, to raw colour values (RGB; RGBW; . . . ) that can be visualized on a display system. In many cases though, the application that generates the image data is assuming that this data will be visualized on a display system with sRGB behaviour and therefore the colour data is just forwarded to the display system.
Most of the colour coding s/colour mappings/colour LUTs that are frequently used to visualize quantitative imaging data, are not perceptually linear. This means that equal steps in the input data (the quantitative value being visualized) will be converted into colour values that are perceived as steps of different magnitude. This is a non-desirable situation since it makes it difficult to visually estimate from the image what exact value of quantitative is being visualized. This can be observed e.g. from FIG. 1, where it is clearly visible that the green part of the colour scale is dominant while in fact (in a perceptually linear situation) all colours should be perceptually equally spread over the scale such that the quantitative value being visualized can be equally easily visually estimated independent of the exact value of the quantitative data. Moreover, even if (in a rare situation) an appropriate colour mapping is used that maps quantitative imaging data onto perceptually linear colour values, even then in the current state-of-the-art typically one does not take into account the (non-linear) behaviour of the display system, resulting into a non-linear perception of the visualized colour values.
In current state-of-the-art, systems comprising a visualization application and a display are not optimized to visualize quantitative colour coded data. For example:                The state-of-the-art does not maximally make use of the capabilities of the display system        The state-of-the-art does not take into account human perception and maximization of perceived contrast        The state-of-the-art is perceptually not uniform, meaning that there are ranges in the scale where it is easier to deduce the values than for other ranges.        
Papers have been published about display calibration methods that calibrate the display in such a way that it will behave as perceptually linear throughout its entire colour gamut, e.g. “Toward a Unified Colour Space for Perception-Based Image Processing”, Ingmar Lissner and Philipp Urban (IEEE Transactions on Image Processing Volume 21 issue 3, March 2012).
In case of perceptually linear display systems, equal distances in the input signal will also result into equal perceptual distance (defined e.g. by deltaE76, deltaE94, deltaE2000, DICOM GSDF JND, JNDMetrix etc.) of the visualized output. So in theory, perfectly perceptually linear displays solve the problem of the non-optimal colour scales (under the condition that an appropriate perceptually linear colour scale is used). However in the state-of-the-art perceptual linearity of a display is achieved by restricting the addressable colour gamut, luminance and contrast of the display such that within these reduced display capabilities a perceptually linear behaviour can be achieved. In other words: existing perceptually linear colour displays require serious compromises in display luminance, display gamut and display contrast. In practice, the lower luminance/contrast/colour gamut makes these displays not useful for many applications.
Patent application EP2620885A2 discloses a method for improving medical images for human perception by selecting a region in the image to improve, and map all points within this region to a linearized scale. Thus EP2620885A2 selects a range of pixel values to be calibrated. Pixel values outside of this are typically clipped to minimum (black) or maximum (white) pixel value. As such image features that are represented by pixel values outside of the selected range will not be visualized anymore after the calibration of EP2620885A2 is applied. EP2620885A2 further explains that selection of the range can be done based on the specific part of the body that is of interest. E.g. EP2620885A2 describes that one could select a “blood vessel” preset (range) that will enhance visualization of blood vessels by selecting a range of pixel values that corresponds to “blood vessels” and mapping pixel values in this range to an optimized scale. However, when “blood vessels” is selected then it will be impossible to perceive image features that are outside of the selected range as these will all be mapped to a single pixel value. So for example, while the “blood vessel” range is selected, it will not be possible to perceive features of bone in the body (since blood vessels and bone typically consist of separated ranges of pixel values).
The deficiency with visualization of colour scales according to state-of-the-art can be illustrated by investigating a commonly used colour scale that ranges from red to yellow. A commonly used perceptually linearity metric is for example deltaE2000.
FIG. 5 shows a perceptual linearity of the “red to yellow” colour scale when visualized on a standard sRGB display, as well as on a DICOM GSDF calibrated display. It can be seen that throughout the entire scale, visualization on neither the sRGB nor the DICOM GSDF calibrated display leads to perceptually linear behaviour. This can be seen from the fact that the deltaE2000 steps are not constant but vary over the entire colour scale. Please note that this shows that despite the fact that a DICOM GSDF calibrated display was used (which means that the display was calibrated to have perceptually linear behaviour for greyscales), the colour behaviour of a commonly used colour scale still is highly perceptually non-linear.
If the display would be calibrated (with methods from prior-art) to be perfectly perceptually linear, then the deltaE2000 step would be constant throughout the entire scale, but there would be a significant loss of luminance, contrast and colour saturation of the display system. The reason why this loss occurs is due to the nature of aiming for perfect perceptually linear (e.g. deltaE2000) calibration: this comes down to trying to position an as large as possible cube inside the total gamut of the display (when visualized in the perceptually linear space). It would be like fitting a cube in the colour space in FIG. 6, and where the display behaviour is then limited to the area within the cube. This would lead to a huge loss in gamut, luminance and contrast.